Search

Abstract: Double gate (DG) FETs have emerged as the most promising technology for sub-50nm transistor design. However, analysis and control of the gate tunneling leakage in DGFET is necessary to fully exploit their advantages. In this paper we have modeled (numerically and analytically) and analyzed gate-to-channel leakage in different DGFET structures, viz., doped body symmetric device (SymDG) with polysilicon gates, intrinsic body symmetric device with metal gates (MGDG) and intrinsic body asymmetric device (AsymDG) with different front and back gate materials. It is observed that, use of (near-mid-gap) metal gate and intrinsic body can result in 3–4× reduction in gate-to-channel leakage compared to the SymDG structure. [Copyright &y& Elsevier]

Spatio-temporal dynamics of physical processes are generally modeled using partial differential equations (PDEs). Though the core dynamics follows some principles of physics, real-world physical processes are often driven by unknown external sources. In such cases, developing a purely analytical model becomes very difficult and data-driven modeling can be of assistance. In this paper, we present a hybrid framework combining physics-based numerical models with deep learning for source identification and forecasting of spatio-temporal dynamical systems with unobservable time-varying external sources. We formulate our model PhICNet as a convolutional recurrent neural network (RNN) which is end-to-end trainable for spatio-temporal evolution prediction of dynamical systems and learns the source behavior as an internal state of the RNN. Experimental results show that the proposed model can forecast the dynamics for a relatively long time and identify the sources as well. [ABSTRACT FROM AUTHOR]

• A new deep learning architecture that integrates spiking neural network (SNN) for contextual information extraction is proposed. • The proposed design shows improved robustness for both random and structured input perturbation during inference. • SNN is implemented with a novel frequency-dependent stochastic spike-timing-dependent-plasticity learning rule. Spiking neural network (SNN) is a type of artificial neural network that uses biologically inspired neuron models and learning rules to develop artificial intelligence with capability parallel to human brain. Deep neural networks (DNNs), on the other hand, uses less biologically plausible neurons and training methods such as gradient descent, and has shown good accuracy in computer vision tasks. However, human brain can still outperform DNN in certain scenarios. For example, DNN experiences significant performance degradation when perturbation from various sources is present in the input, which makes DNN less reliable for systems interacting with physical world. In this paper, we present a hybrid deep net work architecture with s pike-assisted c ontextual i nformation e xtraction (ScieNet) as a solution to the problem. ScieNet integrates a front-end SNN with a novel stochastic spike-timing-dependent plasticity (STDP) algorithm that extracts visual context from images. The back-end DNN is trained for classification given the contextual information. The integrated network demonstrates high resilience to input perturbations without relying on pre-training on perturbed inputs. We demonstrate ScieNet with various back-end DNNs for image classification using different datasets and considering both stochastic and structured input perturbations. Experimental results demonstrate significant improvement in accuracy on perturbed images, while maintaining state-of-the-art accuracy on clean images. [ABSTRACT FROM AUTHOR]